Modelling tree biomass using direct and additive methods with point cloud deep learning in a temperate mixed forest

ABSTRACT: Airborne laser scanning (ALS) data has been widely used for total aboveground tree biomass (AGB) modelling, however, there is less research focusing on estimating specific tree biomass components (wood, branches, bark, and foliage). Knowledge about these biomass components is essential for carbon accounting, understanding forest nutrient cycling, and other applications. In this study, we compare additive AGB estimation (sum of estimated components) with direct AGB estimation using deep neural network (DNN) and random forest (RF) models. We utilise two point cloud DNNs: point-based Dynamic Graph Convolutional Neural Network (DGCNN) and Octree-based Convolutional Neural Network (OCNN). DNN and RF models were trained using a dataset comprised of 2336 sample plots from a mixed temperate forest in New Brunswick, Canada. Results indicate that additive AGB models perform similarly to direct models in terms of coefficient of determination (R2) and root-mean square error (RMSE), and reduced the mean absolute percentage error (MAPE) by 22% on average. Compared to RF, the DNNs provided a small improvement in performance, with OCNN explaining 5% more variation in the data (R2 = 0.76) and reducing MAPE by 20% on average. Overall, this study showcases the effectiveness of additive tree AGB models and highlights the potential of DNNs for enhanced AGB estimation. To further improve DNN performance, we recommend using larger training datasets, implementing hyperparameter optimization, and incorporating additional data such as multispectral imagery

Monitoring boreal forest biomass and carbon storage change by integrating airborne laser scanning, biometry and eddy covariance data

AbstractThis study presents a comparison and integration of three methods commonly used to estimate the amount of forest ecosystem carbon (C) available for storage. In particular, we examine the representation of living above- and below-ground biomass change (net accumulation) using plot-level biometry and repeat airborne laser scanning (ALS) of three dimensional forest plot structure. These are compared with cumulative net CO2 fluxes (net ecosystem production, NEP) from eddy covariance (EC) over a six-year period within a jack pine chronosequence of four stands (~94, 30, 14 and 3years since establishment from 2005) located in central Saskatchewan, Canada. Combining the results of the two methods yield valuable observations on the partitioning of C within ecosystems. Subtracting total living biomass C accumulation from NEP results in a residual that represents change in soil and litter C storage. When plotted against time for the stands investigated, the curve produced is analogous to the soil C dynamics described in Covington (1981). Here, ALS biomass accumulation exceeds EC-based NEP measured in young stands, with the residual declining with age as stands regenerate and litter decomposition stabilizes. During the 50–70year age-period, NEP and live biomass accumulation come into balance, with the soil and litter pools of stands 70–100years post-disturbance becoming a net store of C. Biomass accumulation was greater in 2008–2011 compared to 2005–2008, with the smallest increase in the 94-year-old “old jack pine” stand and greatest in the 14-year-old “harvested jack pine 1994” stand, with values of 1.4 (±3.2) tCha−1 and 12.0 (±1.6) tCha−1, respectively. The efficiency with which CO2 was stored in accumulated biomass was lowest in the youngest and oldest stands, but peaked during rapid regeneration following harvest (14-year-old stand). The analysis highlights that the primary source of uncertainty in the data integration workflow is in the calculation of biomass expansion factors, and this aspect of the workflow needs to be implemented with caution to avoid large error propagations. We suggest that the adoption of integrated ALS, in situ and atmospheric flux monitoring frameworks is needed to improve spatio-temporal partitioning of C balance components at sub-decadal scale within rapidly changing forest ecosystems and for use in national carbon accounting programs

No systematic effects of sampling direction on climate-growth relationships in a large-scale, multi-species tree-ring data set

Ring-width series are important for diverse fields of research such as the study of past climate, forest ecology, forest genetics, and the determination of origin (dendro-provenancing) or dating of archaeological objects. Recent research suggests diverging climate-growth relationships in tree-rings due to the cardinal direction of extracting the tree cores (i.e. direction-specific effect). This presents an understudied source of bias that potentially affects many data sets in tree-ring research. In this study, we investigated possible direction-specific growth variability based on an international (10 countries), multi-species (8 species) tree-ring width network encompassing 22 sites. To estimate the effect of direction-specific growth variability on climate-growth relationships, we applied a combination of three methods: An analysis of signal strength differences, a Principal Component Gradient Analysis and a test on the direction-specific differences in correlations between indexed ring-widths series and climate variables. We found no evidence for systematic direction-specific effects on tree radial growth variability in high-pass filtered ring-width series. In addition, direction-specific growth showed only marginal effects on climate-growth correlations. These findings therefore indicate that there is no consistent bias caused by coring direction in data sets used for diverse dendrochronological applications on relatively mesic sites within forests in flat terrain, as were studied here. However, in extremely dry, warm or cold environments, or on steep slopes, and for different life-forms such as shrubs, further research is advisable.</p